FieldRules captures the reasoning your team carries — the edge cases, the hard-won instinct, the logic that never made it into a spec — and structures it so every decision your product makes is governed by the people who know best. Not the model's defaults.
We’re deploying AI across the product. I have no idea what logic it’s applying to real decisions — or whose it is.
Everything an agent can do, another agent can replicate. The only thing that can't be replicated is the reasoning your people carry about why your company does things differently.
That reasoning — the conditional judgment, the exception-handling logic, the domain expertise that was never written down — is the layer agents can't scrape, can't reverse-engineer, and can't pattern-match from training data.
The first version is what documentation produces. The second is what FieldRules elicits — because it asks “what would go wrong if this rule didn't exist?” instead of “why does this rule exist?”
That single design choice is the difference between compliance and reasoning.
By design, the library doesn’t just collect what experts confirm — it pulls. A PM’s query surfaces a gap, the expert gets a live signal with context, the library grows. Supply meets demand. That’s what makes it a loop.
Most governance tools count rules. FieldRules measures whether the reasoning behind them is real.
The Reasoning Health Score tracks whether your rule library contains genuine operational judgment or just form-filling. It detects tautology, declining depth, missing consequence-naming — the signals that mean your experts are going through the motions instead of thinking.
We're onboarding a small number of teams manually. One pilot customer at a time. If the timing is right for you, let's talk.
No deck. No demo-ware. We start with a conversation.